Blogs

Health care transformation obliges stakeholders in the ecosystem to coordinate. Private and public health plans articulate policy, provider networks deliver care, and patient’s experience care. The glue that holds these stakeholders together is reimbursement. Transformation however requires more than manipulating policies of transactions. This project offers a new way to look at the entire ecosystem using a small set of TennCare data that illustrates how counterfactual methods can serve to extend the scope of information available for transformation.

The presentations of the 2022 Medicaid Managed Care Conference in New Orleans moderated by Virginia Burchett gave an insightful snapshot of the challenges faced by Medicaid programs and vendors. These challenges are diverse, and all are impacted by program design and IT support systems.

The goal of this article is to summarize how HCn3D uses Reinforcement Learning to identify potential outcomes without getting too technical by delving into machine learning functions and algorithms. We won’t be trying to make the reader a “data scientist” or AI expert.

Health Care in 3 Dimensions’ “Cube” is a solution for redirecting health care to changing behaviors. The interest in Artificial Intelligence as applied to health care is growing and expresses the concern if not amazement that technology has had little impact on major national policy goals of access and cost containment.

The Solution for the AI Challenge must be a catalyst for a new theoretical framework. Risk stratification alone will not afford options for actions that will achieve the desired outcomes. Health Care in 3 Dimensions provides the framework to apply theory to practical problems such as reduction in unplanned admissions. This approach will resonate with family physicians and can be tailored to them as the target audience for AI.

The push for new ways to use big data is impacting Medicare and Medicaid plans. Artificial Intelligence is poised to enter the health care arena, and CMS has proposed the AI Health Outcomes Challenge as a contest to incentivize big data specialists to engage the Healthcare Commons. The idea is that tools such as machine learning and Bayesian neural networks can identify actionable structures in data to assist the CMS Innovation Center in testing innovative payment and service delivery models. Part of the contest is to make broad based data available from the Medicare Claims Dataset. The idea of making more CMS data available to the provider community is gaining currency.

All discussions of payment reform require an alert and attentive provider community. Physician burnout is clearly an impediment to this attention. It is estimated that 50% of physicians either have, or are at risk for burnout. The manifestations are protean and apparent to families of the providers and coworkers in the workplace. Not only do the effects impair patient management, but the willingness and enthusiasm to participate in health care reform to guide the engagement pathway from estrangement to emancipation. The Innovation Initiative and the Tenncare Episode of Care model, illustrate a successful program that is an ideal vehicle to accomplish this and show the way to more comprehensive models that will emancipate physicians.

At the heart of the critical realist interpretation of cost containment is to understand the difference in two patient centered approaches to data analytics. The reductionist approach is structured, and the holistic approach is agnostic to how the data is accumulated, or in realist terms the latter approach is open and unstructured. Putting the patient back together again requires a free form method where there is no reason not to draw conclusions from patient centered data because of concerns about statistical significance at the population level. Being unstructured the patient level data can create populations that reflect the holistic approach, in a bottom up method. This is an inversion of the top down approach in the reductionist method that characterizes patients as averages of the predetermined population.

TennCare has released participant commentary on its Technical Advisory Groups. If you are unfamiliar with TennCare’s Episode of Care Program I’d encourage to view TennCare’s Introduction to TennCare Technical Advisory Groups and read my January blog Episode of Care Tenncare which discusses its success and potential to be a model for other Medicaid programs just as it has been for Tennessee’s CMS Innovative Initiative.

This Congress was an assembly of seven summits. These included: Care Coordination and Transitions, Connected Health: RPM and Telehealth, Bundled Payment, ACO Population Health Management, Merit-Bases Incentive Payment System (MIPS), Health Plan Chronic Disease Management, Provider Strategies on Community Health and Social Determinants of Health.